Parkinson’s Disease Reshapes Gut Microbiota Stochasticity and Composition: A Near-Neutral Modeling and Network Analysis

Parkinson’s disease (PD) is increasingly linked to gut microbiome dysbiosis, yet the ecological mechanisms driving these microbial changes remain poorly understood. This study integrates Sloan’s near-neutral model (SNM), the normalized stochasticity ratio (NSR) framework, and ecological network analysis to investigate how PD reshapes gut microbiota stochasticity, composition, and interactions. Using eight publicly available gut microbiome datasets (1,957 samples: 804 healthy, 1,153 PD), we applied SNM to classify species into neutral, positively selected, and negatively selected categories. While the proportions of these categories remained unchanged between healthy and PD groups (neutral: ∼40%, positively selected: ∼47%, negatively selected: ∼13%), NSR analysis revealed holistic alterations in stochasticity due to PD, suggesting increased stochastic drifts. Shared species analysis (SSA) reconciled the apparent inconsistency between SNM and NSR by demonstrating significant compositional shifts within each species category (P < 0.05). Network analysis showed that neutral species had fewer antagonistic interactions (higher positive-to-negative link ratios) compared to selected species, consistent with their ecological equivalence, while negatively selected species dominated in relative abundance across both groups, underscoring their ecological significance. Taylor’s Power Law Extensions (TPLE) indicated invariant heterogeneity-scaling across species categories and disease status, reflecting consistent spatial aggregation patterns. Collectively, these findings reveal that PD reshapes gut microbiota through altered stochasticity and species interactions rather than changes in the overall structure of species categories. This study provides novel ecological insights into PD-associated microbial dysbiosis, highlighting the interplay of stochastic and deterministic processes in microbial community assembly, with potential implications for identifying microbial biomarkers and therapeutic targets.